Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
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Most high-dimensional indexing structures proposed for similarity query in content-based image retrieval (CBIR) systems are tree-structured. The quality of a high-dimensional tree-structured index is mainly determined by its insertion algorithm. Our approach focuses on an important phase in insertion, that is, the tree descending phase, when the tree is explored to find a host node to accommodate the vector to be inserted. We propose to integrate a heuristic algorithm in tree descending in order to find a better host node and thus improve the quality of the resulting index. A heuristic criteria for child selection has been developed, which takes into account both the similarity-based distance and the radius-increasing of the potential host node. Our approach has been implemented and tested on an image database. Our experiments show that the proposed approach can improve the quality of high-dimensional indices without much run-time overhead.